Interactive¶

applymap/apply/map
value_counts
list comprehension hconcat

import pandas as pd
import altair as alt
import numpy as np

Example with data from Spotify¶

Here we use the spotify_dataset.csv file from Canvas. The dataset originally came from Kaggle here. The Kaggle page includes a description of the columns.

We perform some “cleaning” of the dataset. By the end of Math 10, all of the following cell should be understandable, but for now, you shouldn’t worry about the details of this “cleaning”.

Important: You may need to change the path from data/spotify_dataset.csv, depending on where you have this csv file stored.

df = pd.read_csv("data/spotify_dataset.csv") # change path if necessary
df = df.replace(" ",np.nan)
df["Streams"] = df["Streams"].str.replace(",","")
df.iloc[:,[5,7]] = df.iloc[:,[5,7]].apply(pd.to_numeric,axis=0).copy()
df.iloc[:,12:22] = df.iloc[:,12:22].apply(pd.to_numeric,axis=0).copy()

Scatter plot¶

The following Altair chart is just like what we made above with our random DataFrame. We again use the column names to specify which parts of the data to use. Before we used column names like “a” and “b”. Here the column names are more descriptive, like “Energy” and “Loudness”.

df = df[df["Chord"].notna()].copy()

chords = sorted(list(set(df["Chord"])))

chords

['A',
 'A#/Bb',
 'B',
 'C',
 'C#/Db',
 'D',
 'D#/Eb',
 'E',
 'F',
 'F#/Gb',
 'G',
 'G#/Ab']

df["Chord"].value_counts().max()

df["Natural"] = df["Chord"].map(lambda x: 1 if len(x) == 1 else 0)

brush = alt.selection_interval(empty='none')

chart1 = alt.Chart(df).mark_circle().encode(
    x = "Energy",
    y = "Valence",
    color = 'Chord',
    tooltip = ["Artist","Song Name","Release Date","Chord"]
).add_selection(
    brush,
)

chart2 = alt.Chart(df).mark_bar().encode(
    x = alt.X("Chord",scale=alt.Scale(domain=chords)),
    y = alt.Y("count()",scale=alt.Scale(domain=[0,220])),
    color="Chord",
).transform_filter(
    brush,
)

chart1 | chart2

brush = alt.selection_single(empty='none',fields=["Chord"],on='mouseover')

chart1 = alt.Chart(df).mark_circle().encode(
    x = alt.X("Energy",scale=alt.Scale(domain=[0,1])),
    y = alt.Y("Valence",scale=alt.Scale(domain=[0,1])),
    color = 'Chord',
    tooltip = ["Artist","Song Name","Release Date","Chord"]
).transform_filter(
    brush
)

chart2 = alt.Chart(df).mark_bar().encode(
    x = alt.X("Chord",scale=alt.Scale(domain=chords)),
    y = alt.Y("count()",scale=alt.Scale(domain=[0,220])),
    color="Chord",
).add_selection(
    brush,
)

chart1 | chart2

brush = alt.selection_multi(empty='none',fields=["Chord"],on='click')

chart1 = alt.Chart(df).mark_circle().encode(
    x = alt.X("Energy",scale=alt.Scale(domain=[0,1])),
    y = alt.Y("Valence",scale=alt.Scale(domain=[0,1])),
    color = 'Chord',
    tooltip = ["Artist","Song Name","Release Date","Chord"]
).transform_filter(
    brush
)

chart2 = alt.Chart(df).mark_bar().encode(
    x = alt.X("Chord",scale=alt.Scale(domain=chords)),
    y = alt.Y("count()",scale=alt.Scale(domain=[0,220])),
    color="Chord",
).add_selection(
    brush,
)

chart1 | chart2

One of my favorite customizations in Altair is to use a more interesting color scheme. Here is an example using the color scheme “goldred”. You can find more color options in the Vega documentation.

alt.Chart(df).mark_circle().encode(
    x = "Energy",
    y = "Loudness",
    color = alt.Color('Acousticness',scale=alt.Scale(scheme="goldred")),
    tooltip = ["Artist","Song Name","Release Date","Chord"]
)

Sometimes the colors look more natural if they are reversed. We do that by adding reverse=True in the alt.Scale component.

alt.Chart(df).mark_circle().encode(
    x = "Energy",
    y = "Loudness",
    color = alt.Color('Acousticness',scale=alt.Scale(scheme="goldred",reverse=True)),
    tooltip = ["Artist","Song Name","Release Date","Chord"]
)

Histogram¶

Here is an example of how to make a histogram using Altair. The heights of the bars indicate how many total entries there are in that category. The count() entry is not the name of a column. Instead it is a special Altair function to count how often that entry occurs.

alt.Chart(df).mark_bar().encode(
    x = "Artist",
    y = "count()"
)

There are so many artists, this chart is pretty difficult to interpret. Let’s restrict ourselves to the top artists.

Here are the top 19 artists. (Why 19 rather than 20? No great reason, but this particular chart looks better with 19.)

top_artists = df.Artist.value_counts()[:19]
top_artists

Taylor Swift          52
Justin Bieber         32
Lil Uzi Vert          32
Juice WRLD            30
Pop Smoke             29
BTS                   29
Bad Bunny             28
Eminem                22
The Weeknd            21
Drake                 19
Ariana Grande         18
Billie Eilish         18
Selena Gomez          17
J. Cole               16
Doja Cat              16
Dua Lipa              15
Lady Gaga             14
Tyler, The Creator    14
DaBaby                14
Name: Artist, dtype: int64

Let’s make our Altair chart using the sub-DataFrame with just these 19 top artists. We make this using a new pandas method, isin.

df_top = df[df.Artist.isin(top_artists.index)]
df_top.head()

	Index	Highest Charting Position	Number of Times Charted	Week of Highest Charting	Song Name	Streams	Artist	Artist Followers	Song ID	Genre	...	Energy	Loudness	Speechiness	Acousticness	Liveness	Tempo	Duration (ms)	Valence	Chord	Natural
6	7	3	16	2021-05-14--2021-05-21	Kiss Me More (feat. SZA)	29356736	Doja Cat	8640063.0	748mdHapucXQri7IAO8yFK	['dance pop', 'pop']	...	0.701	-3.541	0.0286	0.23500	0.1230	110.968	208867.0	0.742	G#/Ab	0
8	9	3	8	2021-06-18--2021-06-25	Yonaguni	25030128	Bad Bunny	36142273.0	2JPLbjOn0wPCngEot2STUS	['latin', 'reggaeton', 'trap latino']	...	0.648	-4.601	0.1180	0.27600	0.1350	179.951	206710.0	0.440	C#/Db	0
10	11	4	43	2021-05-07--2021-05-14	Levitating (feat. DaBaby)	23518010	Dua Lipa	27142474.0	463CkQjx2Zk1yXoBuierM9	['dance pop', 'pop', 'uk pop']	...	0.825	-3.787	0.0601	0.00883	0.0674	102.977	203064.0	0.915	F#/Gb	0
12	13	5	3	2021-07-09--2021-07-16	Permission to Dance	22062812	BTS	37106176.0	0LThjFY2iTtNdd4wviwVV2	['k-pop', 'k-pop boy group']	...	0.741	-5.330	0.0427	0.00544	0.3370	124.925	187585.0	0.646	A	1
13	14	1	19	2021-04-02--2021-04-09	Peaches (feat. Daniel Caesar & Giveon)	20294457	Justin Bieber	48504126.0	4iJyoBOLtHqaGxP12qzhQI	['canadian pop', 'pop', 'post-teen pop']	...	0.696	-6.181	0.1190	0.32100	0.4200	90.030	198082.0	0.464	C	1

5 rows × 24 columns

alt.Chart(df_top).mark_bar().encode(
    x = "Artist",
    y = "count()"
)

Let’s add color to the chart, using the average number of Streams for each artist. In this example, mean is a special function in Altair, just like count.

Spotify bar chart¶

alt.Chart(df_top).mark_bar().encode(
    x = "Artist",
    y = "count()",
    color = "mean(Streams)"
)

Exercise

Copy the above histogram code, and replace mean with sum. Suddenly the colors are less interesting. Why do you think that is?

Interactive example¶

We end with an example just for inspiration. One of the distinguishing features of Altair is its support for interactivity. If you click and drag on the below chart, the points in the region you select will gain color.

brush = alt.selection_interval(empty='none')

chart = alt.Chart(df).mark_circle().encode(
    x = "Energy",
    y = "Loudness",
    color = alt.condition(brush,
                          alt.Color('Acousticness:Q', scale=alt.Scale(scheme='turbo',reverse=True)),
                          alt.value("lightgrey")),
).add_selection(
    brush,
).properties(
    width = 720,
    height = 450,
    title="Spotify dataset from Kaggle"
)

chart

UC Irvine Math 10

Interactive¶

Example with data from Spotify¶

Scatter plot¶

Spotify chart with tooltip¶

Histogram¶

Spotify bar chart¶

Interactive example¶